Charting public views on the meaning of illness severity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Illness severity is a central principle in multiple priority-setting frameworks, yet there is a paucity of research on public views regarding the meaning of illness severity. This study builds on the findings of a Q methodology study with members of the public that identified four general viewpoints on the meaning of illness severity. Here, we investigate the support for those viewpoints among the Norwegian population. METHODS: Following piloting, the online survey was distributed to a broadly representative sample of the population (March to April 2023). The viewpoints from the earlier Q study were converted into vignettes: Lifespan, Subjective, Objective, and Functioning and Quality of Life (FQoL). The main task in the survey comprised ranking the vignettes and scoring them on a 0-10 visual analogue scale. We describe vignette alignment (from weak to strong) based on four categorisations (C1 to C4). C1 placed all respondents on their top scored vignette(s); C2 required a score of ≥7; C3 was designed to resolve ties; and C4 (which describes vignette membership) required a score of ≥7, a gap of two between vignettes scored ≥7, and did not allow ties. RESULTS: The survey was completed by 1174 individuals; those who completed in ≤3.5 min were excluded. Of the final sample (n = 1094), 98.1% scored at least one vignette ≥7. In C1, 40.2% were aligned with Lifespan, 32.4% with FQoL, 28.9% with Objective, and 16.3% with Subjective. Using the C4 criteria, 55.4% did not have vignette membership, 13.6% had membership with Lifespan, 13.1% with Objective, 11.4% with FQoL, and 6.5% with Subjective. CONCLUSIONS: Severity is an ambiguous term among members of the public. Decisionmakers ought to bear this plurality of meanings in mind, and perhaps reconsider whether using a term as multifaceted as 'severity' is helpful in formulating precise and transparent priority-setting criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it